Overview

Dataset statistics

Number of variables26
Number of observations8161
Missing cells2405
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory208.0 B

Variable types

NUM11
CAT11
BOOL4

Warnings

INCOME has a high cardinality: 6612 distinct values High cardinality
HOME_VAL has a high cardinality: 5106 distinct values High cardinality
BLUEBOOK has a high cardinality: 2789 distinct values High cardinality
OLDCLAIM has a high cardinality: 2857 distinct values High cardinality
YOJ has 454 (5.6%) missing values Missing
INCOME has 445 (5.5%) missing values Missing
HOME_VAL has 464 (5.7%) missing values Missing
JOB has 526 (6.4%) missing values Missing
CAR_AGE has 510 (6.2%) missing values Missing
INDEX has unique values Unique
TARGET_AMT has 6008 (73.6%) zeros Zeros
KIDSDRIV has 7180 (88.0%) zeros Zeros
HOMEKIDS has 5289 (64.8%) zeros Zeros
YOJ has 625 (7.7%) zeros Zeros
CLM_FREQ has 5009 (61.4%) zeros Zeros
MVR_PTS has 3712 (45.5%) zeros Zeros

Reproduction

Analysis started2021-01-31 19:47:13.393764
Analysis finished2021-01-31 19:47:43.644006
Duration30.25 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

INDEX
Real number (ℝ≥0)

UNIQUE

Distinct8161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5151.867663
Minimum1
Maximum10302
Zeros0
Zeros (%)0.0%
Memory size63.8 KiB
2021-01-31T20:47:43.772519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile509
Q12559
median5133
Q37745
95-th percentile9791
Maximum10302
Range10301
Interquartile range (IQR)5186

Descriptive statistics

Standard deviation2978.893962
Coefficient of variation (CV)0.57821632
Kurtosis-1.20298272
Mean5151.867663
Median Absolute Deviation (MAD)2591
Skewness0.002004613662
Sum42044392
Variance8873809.235
MonotocityStrictly increasing
2021-01-31T20:47:43.942564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
95661< 0.1%
 
33951< 0.1%
 
54481< 0.1%
 
74971< 0.1%
 
13541< 0.1%
 
34031< 0.1%
 
95501< 0.1%
 
54561< 0.1%
 
75051< 0.1%
 
Other values (8151)815199.9%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
41< 0.1%
 
51< 0.1%
 
61< 0.1%
 
ValueCountFrequency (%) 
103021< 0.1%
 
103011< 0.1%
 
102991< 0.1%
 
102981< 0.1%
 
102971< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.8 KiB
0
6008 
1
2153 
ValueCountFrequency (%) 
0600873.6%
 
1215326.4%
 
2021-01-31T20:47:44.053513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

TARGET_AMT
Real number (ℝ≥0)

ZEROS

Distinct1949
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1504.324648
Minimum0
Maximum107586.1362
Zeros6008
Zeros (%)73.6%
Memory size63.8 KiB
2021-01-31T20:47:44.158865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31036
95-th percentile6452
Maximum107586.1362
Range107586.1362
Interquartile range (IQR)1036

Descriptive statistics

Standard deviation4704.02693
Coefficient of variation (CV)3.127002496
Kurtosis112.3862763
Mean1504.324648
Median Absolute Deviation (MAD)0
Skewness8.70950474
Sum12276793.45
Variance22127869.36
MonotocityNot monotonic
2021-01-31T20:47:44.336389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0600873.6%
 
23274< 0.1%
 
54533< 0.1%
 
25463< 0.1%
 
35013< 0.1%
 
36673< 0.1%
 
57283< 0.1%
 
56923< 0.1%
 
33503< 0.1%
 
9803< 0.1%
 
Other values (1939)212526.0%
 
ValueCountFrequency (%) 
0600873.6%
 
30.277280151< 0.1%
 
58.531062311< 0.1%
 
95.567317171< 0.1%
 
108.74149861< 0.1%
 
ValueCountFrequency (%) 
107586.13621< 0.1%
 
85523.653351< 0.1%
 
78874.190561< 0.1%
 
77907.430281< 0.1%
 
73783.465921< 0.1%
 

KIDSDRIV
Real number (ℝ≥0)

ZEROS

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1710574684
Minimum0
Maximum4
Zeros7180
Zeros (%)88.0%
Memory size63.8 KiB
2021-01-31T20:47:44.474418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5115340939
Coefficient of variation (CV)2.99042245
Kurtosis11.79177272
Mean0.1710574684
Median Absolute Deviation (MAD)0
Skewness3.353069928
Sum1396
Variance0.2616671292
MonotocityNot monotonic
2021-01-31T20:47:44.588075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
0718088.0%
 
16367.8%
 
22793.4%
 
3620.8%
 
44< 0.1%
 
ValueCountFrequency (%) 
0718088.0%
 
16367.8%
 
22793.4%
 
3620.8%
 
44< 0.1%
 
ValueCountFrequency (%) 
44< 0.1%
 
3620.8%
 
22793.4%
 
16367.8%
 
0718088.0%
 

AGE
Real number (ℝ≥0)

Distinct60
Distinct (%)0.7%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean44.79031269
Minimum16
Maximum81
Zeros0
Zeros (%)0.0%
Memory size63.8 KiB
2021-01-31T20:47:44.747287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile30
Q139
median45
Q351
95-th percentile59
Maximum81
Range65
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.627589456
Coefficient of variation (CV)0.1926217733
Kurtosis-0.06028251742
Mean44.79031269
Median Absolute Deviation (MAD)6
Skewness-0.02899961562
Sum365265
Variance74.43529982
MonotocityNot monotonic
2021-01-31T20:47:44.907942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
464014.9%
 
453764.6%
 
483634.4%
 
473554.3%
 
433514.3%
 
413364.1%
 
443364.1%
 
423334.1%
 
503294.0%
 
403173.9%
 
Other values (50)465857.1%
 
ValueCountFrequency (%) 
1650.1%
 
171< 0.1%
 
183< 0.1%
 
1950.1%
 
203< 0.1%
 
ValueCountFrequency (%) 
811< 0.1%
 
801< 0.1%
 
761< 0.1%
 
733< 0.1%
 
723< 0.1%
 

HOMEKIDS
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7212351428
Minimum0
Maximum5
Zeros5289
Zeros (%)64.8%
Memory size63.8 KiB
2021-01-31T20:47:45.254318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.116323291
Coefficient of variation (CV)1.547793812
Kurtosis0.6510197811
Mean0.7212351428
Median Absolute Deviation (MAD)0
Skewness1.341620234
Sum5886
Variance1.24617769
MonotocityNot monotonic
2021-01-31T20:47:45.380017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
0528964.8%
 
2111813.7%
 
190211.1%
 
36748.3%
 
41642.0%
 
5140.2%
 
ValueCountFrequency (%) 
0528964.8%
 
190211.1%
 
2111813.7%
 
36748.3%
 
41642.0%
 
ValueCountFrequency (%) 
5140.2%
 
41642.0%
 
36748.3%
 
2111813.7%
 
190211.1%
 

YOJ
Real number (ℝ≥0)

MISSING
ZEROS

Distinct21
Distinct (%)0.3%
Missing454
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean10.49928636
Minimum0
Maximum23
Zeros625
Zeros (%)7.7%
Memory size63.8 KiB
2021-01-31T20:47:45.499222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median11
Q313
95-th percentile15
Maximum23
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.092474183
Coefficient of variation (CV)0.3897859379
Kurtosis1.179968995
Mean10.49928636
Median Absolute Deviation (MAD)2
Skewness-1.203436037
Sum80918
Variance16.74834494
MonotocityNot monotonic
2021-01-31T20:47:45.630827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
12115814.2%
 
13101612.4%
 
11100312.3%
 
147859.6%
 
107499.2%
 
06257.7%
 
95216.4%
 
154635.7%
 
83844.7%
 
73003.7%
 
Other values (11)7038.6%
 
(Missing)4545.6%
 
ValueCountFrequency (%) 
06257.7%
 
160.1%
 
2150.2%
 
3360.4%
 
4370.5%
 
ValueCountFrequency (%) 
232< 0.1%
 
19120.1%
 
18250.3%
 
171011.2%
 
162042.5%
 

INCOME
Categorical

HIGH CARDINALITY
MISSING

Distinct6612
Distinct (%)85.7%
Missing445
Missing (%)5.5%
Memory size63.8 KiB
$0
 
615
$61,790
 
4
$48,509
 
4
$26,840
 
4
$47,513
 
3
Other values (6607)
7086 
ValueCountFrequency (%) 
$06157.5%
 
$61,7904< 0.1%
 
$48,5094< 0.1%
 
$26,8404< 0.1%
 
$47,5133< 0.1%
 
$183,2963< 0.1%
 
$143,0733< 0.1%
 
$7,9713< 0.1%
 
$63,3573< 0.1%
 
$50,1663< 0.1%
 
Other values (6602)707186.6%
 
(Missing)4455.5%
 
2021-01-31T20:47:45.808417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6156 ?
Unique (%)79.8%
2021-01-31T20:47:45.966977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length7
Mean length6.519666708
Min length2

PARENT1
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.8 KiB
No
7084 
Yes
1077 
ValueCountFrequency (%) 
No708486.8%
 
Yes107713.2%
 
2021-01-31T20:47:46.057337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

HOME_VAL
Categorical

HIGH CARDINALITY
MISSING

Distinct5106
Distinct (%)66.3%
Missing464
Missing (%)5.7%
Memory size63.8 KiB
$0
2294 
$159,568
 
3
$153,061
 
3
$238,724
 
3
$288,592
 
3
Other values (5101)
5391 
ValueCountFrequency (%) 
$0229428.1%
 
$159,5683< 0.1%
 
$153,0613< 0.1%
 
$238,7243< 0.1%
 
$288,5923< 0.1%
 
$123,1093< 0.1%
 
$332,6733< 0.1%
 
$111,1293< 0.1%
 
$166,4813< 0.1%
 
$196,3203< 0.1%
 
Other values (5096)537665.9%
 
(Missing)4645.7%
 
2021-01-31T20:47:46.183837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4819 ?
Unique (%)62.6%
2021-01-31T20:47:46.327375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length5.983702978
Min length2

MSTATUS
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.8 KiB
Yes
4894 
z_No
3267 
ValueCountFrequency (%) 
Yes489460.0%
 
z_No326740.0%
 
2021-01-31T20:47:46.464158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-31T20:47:46.558665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:46.655785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.400318588
Min length3

SEX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.8 KiB
z_F
4375 
M
3786 
ValueCountFrequency (%) 
z_F437553.6%
 
M378646.4%
 
2021-01-31T20:47:46.783020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-31T20:47:46.869897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:46.964240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.072172528
Min length1

EDUCATION
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.8 KiB
z_High School
2330 
Bachelors
2242 
Masters
1658 
<High School
1203 
PhD
728 
ValueCountFrequency (%) 
z_High School233028.6%
 
Bachelors224227.5%
 
Masters165820.3%
 
<High School120314.7%
 
PhD7288.9%
 
2021-01-31T20:47:47.107872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-31T20:47:47.206902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:47.328219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length9
Mean length9.642690847
Min length3

JOB
Categorical

MISSING

Distinct8
Distinct (%)0.1%
Missing526
Missing (%)6.4%
Memory size63.8 KiB
z_Blue Collar
1825 
Clerical
1271 
Professional
1117 
Manager
988 
Lawyer
835 
Other values (3)
1599 
ValueCountFrequency (%) 
z_Blue Collar182522.4%
 
Clerical127115.6%
 
Professional111713.7%
 
Manager98812.1%
 
Lawyer83510.2%
 
Student7128.7%
 
Home Maker6417.9%
 
Doctor2463.0%
 
(Missing)5266.4%
 
2021-01-31T20:47:47.464574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-31T20:47:47.565566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:47.713716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length8
Mean length9.027202549
Min length3

TRAVTIME
Real number (ℝ≥0)

Distinct97
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.48572479
Minimum5
Maximum142
Zeros0
Zeros (%)0.0%
Memory size63.8 KiB
2021-01-31T20:47:47.845051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q122
median33
Q344
95-th percentile60
Maximum142
Range137
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.90833341
Coefficient of variation (CV)0.4750780672
Kurtosis0.6663746248
Mean33.48572479
Median Absolute Deviation (MAD)11
Skewness0.4469816868
Sum273277
Variance253.0750719
MonotocityNot monotonic
2021-01-31T20:47:48.005370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
53344.1%
 
352192.7%
 
302192.7%
 
322142.6%
 
252142.6%
 
362112.6%
 
292072.5%
 
332062.5%
 
242042.5%
 
372022.5%
 
Other values (87)593172.7%
 
ValueCountFrequency (%) 
53344.1%
 
6490.6%
 
7430.5%
 
8540.7%
 
9700.9%
 
ValueCountFrequency (%) 
1421< 0.1%
 
1341< 0.1%
 
1241< 0.1%
 
1131< 0.1%
 
1031< 0.1%
 

CAR_USE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.8 KiB
Private
5132 
Commercial
3029 
ValueCountFrequency (%) 
Private513262.9%
 
Commercial302937.1%
 
2021-01-31T20:47:48.200415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-31T20:47:48.276236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:48.368233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length7
Mean length8.113466487
Min length7

BLUEBOOK
Categorical

HIGH CARDINALITY

Distinct2789
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Memory size63.8 KiB
$1,500
 
157
$6,000
 
34
$5,800
 
33
$6,200
 
33
$6,400
 
31
Other values (2784)
7873 
ValueCountFrequency (%) 
$1,5001571.9%
 
$6,000340.4%
 
$5,800330.4%
 
$6,200330.4%
 
$6,400310.4%
 
$6,100300.4%
 
$5,900300.4%
 
$6,500290.4%
 
$5,400280.3%
 
$5,700260.3%
 
Other values (2779)773094.7%
 
2021-01-31T20:47:48.545592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique900 ?
Unique (%)11.0%
2021-01-31T20:47:48.700727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length6.717068987
Min length6

TIF
Real number (ℝ≥0)

Distinct23
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.351304987
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Memory size63.8 KiB
2021-01-31T20:47:48.817924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q37
95-th percentile13
Maximum25
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.146635309
Coefficient of variation (CV)0.7748830088
Kurtosis0.4243279295
Mean5.351304987
Median Absolute Deviation (MAD)3
Skewness0.891139559
Sum43672
Variance17.19458439
MonotocityNot monotonic
2021-01-31T20:47:48.950845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%) 
1253331.0%
 
6134116.4%
 
4124215.2%
 
107809.6%
 
76207.6%
 
34245.2%
 
132783.4%
 
112423.0%
 
92252.8%
 
171041.3%
 
Other values (13)3724.6%
 
ValueCountFrequency (%) 
1253331.0%
 
260.1%
 
34245.2%
 
4124215.2%
 
5520.6%
 
ValueCountFrequency (%) 
252< 0.1%
 
223< 0.1%
 
21110.1%
 
2080.1%
 
1980.1%
 

CAR_TYPE
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.8 KiB
z_SUV
2294 
Minivan
2145 
Pickup
1389 
Sports Car
907 
Van
750 
ValueCountFrequency (%) 
z_SUV229428.1%
 
Minivan214526.3%
 
Pickup138917.0%
 
Sports Car90711.1%
 
Van7509.2%
 
Panel Truck6768.3%
 
2021-01-31T20:47:49.095558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-31T20:47:49.193433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:49.326790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length6
Mean length6.564759221
Min length3

RED_CAR
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.8 KiB
no
5783 
yes
2378 
ValueCountFrequency (%) 
no578370.9%
 
yes237829.1%
 
2021-01-31T20:47:49.417712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

OLDCLAIM
Categorical

HIGH CARDINALITY

Distinct2857
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Memory size63.8 KiB
$0
5009 
$4,263
 
4
$1,310
 
4
$1,391
 
4
$3,338
 
3
Other values (2852)
3137 
ValueCountFrequency (%) 
$0500961.4%
 
$4,2634< 0.1%
 
$1,3104< 0.1%
 
$1,3914< 0.1%
 
$3,3383< 0.1%
 
$1,1053< 0.1%
 
$4,5673< 0.1%
 
$3,0683< 0.1%
 
$4,4483< 0.1%
 
$5,2893< 0.1%
 
Other values (2847)312238.3%
 
2021-01-31T20:47:49.547473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2592 ?
Unique (%)31.8%
2021-01-31T20:47:49.689228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length2
Mean length3.614753094
Min length2

CLM_FREQ
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7985540988
Minimum0
Maximum5
Zeros5009
Zeros (%)61.4%
Memory size63.8 KiB
2021-01-31T20:47:49.788541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.158452681
Coefficient of variation (CV)1.450687791
Kurtosis0.2860042955
Mean0.7985540988
Median Absolute Deviation (MAD)0
Skewness1.209242991
Sum6517
Variance1.342012615
MonotocityNot monotonic
2021-01-31T20:47:49.900025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
0500961.4%
 
2117114.3%
 
199712.2%
 
37769.5%
 
41902.3%
 
5180.2%
 
ValueCountFrequency (%) 
0500961.4%
 
199712.2%
 
2117114.3%
 
37769.5%
 
41902.3%
 
ValueCountFrequency (%) 
5180.2%
 
41902.3%
 
37769.5%
 
2117114.3%
 
199712.2%
 

REVOKED
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.8 KiB
No
7161 
Yes
1000 
ValueCountFrequency (%) 
No716187.7%
 
Yes100012.3%
 
2021-01-31T20:47:49.990703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

MVR_PTS
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.695503002
Minimum0
Maximum13
Zeros3712
Zeros (%)45.5%
Memory size63.8 KiB
2021-01-31T20:47:50.064870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum13
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.147111744
Coefficient of variation (CV)1.266356793
Kurtosis1.378141796
Mean1.695503002
Median Absolute Deviation (MAD)1
Skewness1.348335868
Sum13837
Variance4.610088843
MonotocityNot monotonic
2021-01-31T20:47:50.192572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
0371245.5%
 
1115714.2%
 
294811.6%
 
37589.3%
 
45997.3%
 
53994.9%
 
62663.3%
 
71672.0%
 
8841.0%
 
9450.6%
 
Other values (3)260.3%
 
ValueCountFrequency (%) 
0371245.5%
 
1115714.2%
 
294811.6%
 
37589.3%
 
45997.3%
 
ValueCountFrequency (%) 
132< 0.1%
 
11110.1%
 
10130.2%
 
9450.6%
 
8841.0%
 

CAR_AGE
Real number (ℝ)

MISSING

Distinct30
Distinct (%)0.4%
Missing510
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean8.328323095
Minimum-3
Maximum28
Zeros3
Zeros (%)< 0.1%
Memory size63.8 KiB
2021-01-31T20:47:50.319103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile1
Q11
median8
Q312
95-th percentile18
Maximum28
Range31
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.70074244
Coefficient of variation (CV)0.6845006341
Kurtosis-0.7480917592
Mean8.328323095
Median Absolute Deviation (MAD)5
Skewness0.2820637171
Sum63720
Variance32.49846436
MonotocityNot monotonic
2021-01-31T20:47:50.447005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%) 
1193423.7%
 
85376.6%
 
95266.4%
 
75246.4%
 
104695.7%
 
114605.6%
 
64515.5%
 
123684.5%
 
133564.4%
 
143113.8%
 
Other values (20)171521.0%
 
(Missing)5106.2%
 
ValueCountFrequency (%) 
-31< 0.1%
 
03< 0.1%
 
1193423.7%
 
2120.1%
 
3540.7%
 
ValueCountFrequency (%) 
281< 0.1%
 
271< 0.1%
 
262< 0.1%
 
2560.1%
 
24100.1%
 

URBANICITY
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.8 KiB
Highly Urban/ Urban
6492 
z_Highly Rural/ Rural
1669 
ValueCountFrequency (%) 
Highly Urban/ Urban649279.5%
 
z_Highly Rural/ Rural166920.5%
 
2021-01-31T20:47:50.581404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-31T20:47:50.656579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:50.758441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length21
Median length19
Mean length19.4090185
Min length19

Interactions

2021-01-31T20:47:18.325918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:18.543414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:18.783474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:18.949345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:19.165631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:19.368383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:19.599830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:19.849640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:20.052010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:20.246794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:20.441643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:20.648775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:20.839164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:21.019270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:21.248956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:21.403769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:21.562852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:21.721728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:22.008177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:22.206677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:22.416740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:22.815352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:22.979931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:23.165359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:23.356165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:23.548580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:23.722471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:23.922400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:24.101038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:24.323383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:24.535886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:24.761402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:24.987954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:25.252332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:25.459527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:25.639527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:25.855480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:26.052149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:26.260246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:26.523223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:26.857338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:27.102075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:27.315402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:27.533430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:27.782264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:27.973888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:28.195852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:28.405805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:28.602956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:28.810431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:28.966155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:29.144948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:29.303183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:29.458793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:29.752057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:29.905675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:30.051568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:30.199539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:30.397512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:30.567176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:30.723195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:30.907975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:31.123040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:31.326393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:31.533166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:31.727943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:31.915189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:32.216066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:32.425904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:32.601357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:32.774746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:32.941699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:33.108135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:33.301381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:33.529043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:33.708616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:33.898789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:34.143054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:34.340445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:34.491918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:34.659171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:34.805107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:34.955059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:35.110257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:35.325412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:35.512915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:35.708102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:35.900933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:36.052422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:36.208281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:36.407619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:36.599041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:36.808839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:37.003040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:37.154085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:37.525183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:37.705226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:37.882332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:38.026397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:38.165526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:38.310504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:38.448075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:38.594708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:38.747781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:38.911937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:39.058771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:39.211404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:39.378693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:39.562126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:39.737682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:39.941547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:40.096503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:40.252712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:40.427792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:40.589784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:40.729229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:40.860362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:41.005107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:41.157411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:41.286423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:41.418435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-01-31T20:47:50.894569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-31T20:47:51.139822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-31T20:47:51.673216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-31T20:47:51.946066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-01-31T20:47:52.217049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-01-31T20:47:41.826814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:42.919390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:43.241536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-31T20:47:43.426429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

INDEXTARGET_FLAGTARGET_AMTKIDSDRIVAGEHOMEKIDSYOJINCOMEPARENT1HOME_VALMSTATUSSEXEDUCATIONJOBTRAVTIMECAR_USEBLUEBOOKTIFCAR_TYPERED_CAROLDCLAIMCLM_FREQREVOKEDMVR_PTSCAR_AGEURBANICITY
0100.0060.0011.0$67,349No$0z_NoMPhDProfessional14Private$14,23011Minivanyes$4,4612No318.0Highly Urban/ Urban
1200.0043.0011.0$91,449No$257,252z_NoMz_High Schoolz_Blue Collar22Commercial$14,9401Minivanyes$00No01.0Highly Urban/ Urban
2400.0035.0110.0$16,039No$124,191Yesz_Fz_High SchoolClerical5Private$4,0104z_SUVno$38,6902No310.0Highly Urban/ Urban
3500.0051.0014.0NaNNo$306,251YesM<High Schoolz_Blue Collar32Private$15,4407Minivanyes$00No06.0Highly Urban/ Urban
4600.0050.00NaN$114,986No$243,925Yesz_FPhDDoctor36Private$18,0001z_SUVno$19,2172Yes317.0Highly Urban/ Urban
5712946.0034.0112.0$125,301Yes$0z_Noz_FBachelorsz_Blue Collar46Commercial$17,4301Sports Carno$00No07.0Highly Urban/ Urban
6800.0054.00NaN$18,755NoNaNYesz_F<High Schoolz_Blue Collar33Private$8,7801z_SUVno$00No01.0Highly Urban/ Urban
71114021.0137.02NaN$107,961No$333,680YesMBachelorsz_Blue Collar44Commercial$16,9701Vanyes$2,3741Yes107.0Highly Urban/ Urban
81212501.0034.0010.0$62,978No$0z_Noz_FBachelorsClerical34Private$11,2001z_SUVno$00No01.0Highly Urban/ Urban
91300.0050.007.0$106,952No$0z_NoMBachelorsProfessional48Commercial$18,5107Vanno$00No117.0z_Highly Rural/ Rural

Last rows

INDEXTARGET_FLAGTARGET_AMTKIDSDRIVAGEHOMEKIDSYOJINCOMEPARENT1HOME_VALMSTATUSSEXEDUCATIONJOBTRAVTIMECAR_USEBLUEBOOKTIFCAR_TYPERED_CAROLDCLAIMCLM_FREQREVOKEDMVR_PTSCAR_AGEURBANICITY
81511029100.0054.0013.0$81,818No$272,725YesMBachelorsManager18Commercial$19,6601Vanno$24,6901Yes64.0Highly Urban/ Urban
81521029200.0146.0012.0$45,018No$0z_NoMz_High Schoolz_Blue Collar26Private$15,0604Minivanno$33,0263No01.0z_Highly Rural/ Rural
81531029300.0048.0010.0$111,305No$0z_Noz_FPhDDoctor59Private$17,43013z_SUVno$00No418.0Highly Urban/ Urban
81541029500.0138.0416.0$12,717No$0Yesz_FBachelorsStudent15Commercial$24,7401Pickupno$9,2453No315.0Highly Urban/ Urban
81551029600.0041.007.0$6,256No$0z_NoMz_High SchoolStudent41Private$5,6001Pickupno$00No07.0z_Highly Rural/ Rural
81561029700.0035.0011.0$43,112No$0z_NoMz_High Schoolz_Blue Collar51Commercial$27,33010Panel Truckyes$00No08.0z_Highly Rural/ Rural
81571029800.0145.029.0$164,669No$386,273YesMPhDManager21Private$13,27015Minivanno$00No217.0Highly Urban/ Urban
81581029900.0046.009.0$107,204No$332,591YesMMastersNaN36Commercial$24,4906Panel Truckno$00No01.0Highly Urban/ Urban
81591030100.0050.007.0$43,445No$149,248Yesz_FBachelorsHome Maker36Private$22,5506Minivanno$00No011.0Highly Urban/ Urban
81601030200.0052.0011.0$53,235No$197,017Yesz_Fz_High SchoolClerical64Private$19,4006Minivanno$00No09.0z_Highly Rural/ Rural